BronchoPose: an analysis of data and model configuration for vision-based bronchoscopy pose estimation

TL;DR

This paper introduces a synthetic dataset and standardized evaluation framework to compare neural network architectures for vision-based bronchoscopy pose estimation, leading to improved accuracy in camera orientation measurement.

Contribution

It provides a novel synthetic dataset, a standardized comparison framework, and evaluates multiple neural network architectures for better pose estimation in bronchoscopy.

Findings

Proposed metric improves orientation accuracy

Standardized conditions enable fair comparison of models

Certain architectures outperform previous methods

Abstract

Vision-based bronchoscopy (VB) models require the registration of the virtual lung model with the frames from the video bronchoscopy to provide effective guidance during the biopsy. The registration can be achieved by either tracking the position and orientation of the bronchoscopy camera or by calibrating its deviation from the pose (position and orientation) simulated in the virtual lung model. Recent advances in neural networks and temporal image processing have provided new opportunities for guided bronchoscopy. However, such progress has been hindered by the lack of comparative experimental conditions. In the present paper, we share a novel synthetic dataset allowing for a fair comparison of methods. Moreover, this paper investigates several neural network architectures for the learning of temporal information at different levels of subject personalization. In order to improve orientation measurement, we also present a standardized comparison framework and a novel metric for camera orientation learning. Results on the dataset show that the proposed metric and architectures, as well as the standardized conditions, provide notable improvements to current state-of-the-art camera pose estimation in video bronchoscopy.